Automatic Detection and Quantification of the Aorta from Medical Images

ABSTRACT

Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

TECHNICAL FIELD

The present invention relates generally to detection and quantificationof an aorta of a patient from medical images, and more particularly tothe automatic detection and quantification of the aorta using aplurality of machine learning models to analyze medical images.

BACKGROUND

Early detection is critical for many aortic diseases, such as aorticdissection, aortic rupture, and ruptured abdominal aortic aneurysms.Left untreated, such aortic diseases have severe and potentially fatalconsequences. Conventionally, such aortic diseases are diagnosed bymanual evaluation of medical images of a patient. However, such aorticdiseases are often asymptomatic and are only detected aftercomplications have manifested. It is therefore difficult to diagnosesuch aortic diseases before complications have manifested, resulting ina delayed diagnosis or a failure to diagnose.

BRIEF SUMMARY OF THE INVENTION

In accordance with one or more embodiments, systems and methods areprovided for evaluating an aorta of a patient. A medical image of anaorta of a patient is received and the aorta is segmented from themedical image. A measurement plane is identified on the segmented aortaand a measurement is calculated at the measurement plane. The aorta ofthe patient is evaluated based on the measurement calculated at themeasurement plane. Results of the calculated measurement and/or theevaluated aorta may be caused to be displayed to a user on a displaydevice. Advantageously, the embodiments described herein provide for theautomatic detection and quantification of the aorta by applying machinelearning models to medical images, even if such medical images were notacquired to diagnose aortic diseases.

In accordance with one or more embodiments, the measurement plane isidentified on the segmented aorta by identifying an aortic centerline ofthe aorta and identifying a location on the aortic centerline and acorresponding plane normal vector as the measurement plane. Thesegmented aorta and the aortic centerline may be jointly and iterativelydetermined by refining the segmented aorta based on the aorticcenterline and refining the aortic centerline based on the refinedsegmented aorta.

In accordance with one or more embodiments, the measurement calculatedat the measurement plane is a diameter of the segmented aorta at themeasurement plane. For example, the diameter may include a minimumdiameter, a maximum diameter, and/or an average diameter of thesegmented aorta at the measurement plane.

In accordance with one or more embodiments, the aorta of the patient isevaluated by comparing the measurement calculated at the measurementplane with a patient-specific range. An alert may be generatedindicating results of the evaluating in response to the comparing. Arisk score may also be calculated in response to the comparing and aclinical treatment plan may be caused to be presented to a user based onthe risk score.

In accordance with one or more embodiments, an apparatus is provided forevaluating an aorta of a patient. The apparatus comprises means forreceiving a medical image of an aorta of a patient, means for segmentingthe aorta from the medical image, means for identifying one or moremeasurement planes on the segmented aorta, means for calculating atleast one measurement at each of the one or more measurement planes; andmeans for evaluating the aorta of the patient based on the at least onemeasurement calculated at each of the one or more measurement planes.

In accordance with one or more embodiments, a non-transitory computerreadable medium is provided. The non-transitory computer readable mediumstores computer program instructions for evaluating an aorta of apatient. The computer program instructions when executed by a processorcause the processor to perform operations of receiving a medical imageof an aorta of a patient, segmenting the aorta from the medical image,identifying one or more measurement planes on the segmented aorta,calculating at least one measurement at each of the one or moremeasurement planes, and evaluating the aorta of the patient based on theat least one measurement calculated at each of the one or moremeasurement planes.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative system for evaluating an aorta of apatient, in accordance with one or more embodiments;

FIG. 2 shows a high-level workflow for evaluating an aorta of a patient,in accordance with one or more embodiments;

FIG. 3 shows a method for evaluating an aorta of a patient, inaccordance with one or more embodiments;

FIG. 4 shows a workflow for training and applying a machine learningmodel for segmenting an aorta from a medical image, in accordance withone or more embodiments;

FIG. 5 shows a workflow of a joint and iterative process for segmentingan aorta from a medical image and computing an aortic centerline of theaorta, in accordance with one or more embodiments;

FIG. 6 shows an illustrative user interface depicting measurements andresults of an evaluation of an aorta, in accordance with one or moreembodiments; and

FIG. 7 shows a high-level block diagram of a computer.

DETAILED DESCRIPTION

The present invention generally relates to methods and systems for theautomatic detection and quantification of the aorta from medical images.Embodiments of the present invention are described herein to give avisual understanding of such methods and systems. A digital image isoften composed of digital representations of one or more objects (orshapes). The digital representation of an object is often describedherein in terms of identifying and manipulating the objects. Suchmanipulations are virtual manipulations accomplished in the memory orother circuitry/hardware of a computer system. Accordingly, is to beunderstood that embodiments of the present invention may be performed bya computer system using data stored within the computer system.

FIG. 1 shows a system 100 configured to evaluate an aorta of a patient,in accordance with one or more embodiments. System 100 includesworkstation 102, which may be used for assisting a clinician (e.g., adoctor, a medical professional, or any other user) in performing amedical evaluation of a patient 106 (or any other subject). Workstation102 may be implemented using any suitable computing device, such as,e.g., computer 702 of FIG. 7.

In one embodiment, workstation 102 may assist the clinician inperforming a medical evaluation of patient 106. The medical evaluationof patient 106 may be any medical evaluation of patient 106, includingan evaluation related to a diagnosis of aortic diseases of patient 106or an evaluation unrelated to a diagnosis of aortic diseases of patient106. Accordingly, workstation 102 may receive medical images of patient106 from one or more medical imaging systems 104. Medical imaging system104 may be of any modality, such as, e.g., a two-dimensional (2D) orthree-dimensional (3D) computed tomography (CT), x-ray, magneticresonance imaging (MRI), ultrasound (US), single-photon emissioncomputed tomography (SPECT), positron emission tomography (PET), or anyother suitable modality or combination of modalities. In anotherembodiment, workstation 102 may receive the images by loading previouslystored images of the patient acquired using medical imaging system 104.

Embodiments of the present invention provide for the automatic detectionand quantification of the aorta of patient 106 by applying a pluralityof machine learning models to analyze medical images. Advantageously,embodiments of the present invention enable fast and automatic analysisof medical images to evaluate aortic diseases of patient 106 and provideearly detection of aortic diseases before symptoms or complicationsmanifest. Such fast and automatic analysis allows for the analysis ofroutine medical images acquired while performing a medical evaluationunrelated to the diagnosis of aortic diseases of patient 106.Accordingly, while conventional, manual analysis of such medical imagesacquired for a medical evaluation unrelated to aortic diseases cannotpractically be performed, embodiments of the present invention providefor fast and automatic analysis of medical images, thereby providing foran improvement in computers and computer related technology.

It should be understood that while the embodiments discussed herein maybe described with respect to analyzing medical images to evaluate aorticdiseases of a patient, the present invention is not so limited.Embodiments of the present invention may be applied for analyzing anytype of image for any measure of interest.

FIG. 2 shows a high-level workflow 200 for evaluating an aorta of apatient, in accordance with one or more embodiments. In workflow 200, aCT medical image 202 of a chest of a patient is acquired. Aorta 204 issegmented from medical image 202. A plurality of measuring planes 206are identified on segmented aorta 204. At each measuring plane 206, oneor more diameters of segmented aorta 204 are calculated. The one or morediameters may be compared to a normal range of diameters for the patientto evaluate the aorta. For example, diameters outside the normal rangemay indicate abnormalities in the aorta. Workflow 200 thereby providesautomatic detection and quantification of the aorta of the patient.

FIG. 3 shows a method 300 for evaluating an aorta of a patient, inaccordance with one or more embodiments. Method 300 will be discussedwith respect to system 100 of FIG. 1. In one embodiment, the steps ofmethod 300 are performed by workstation 102 of FIG. 1.

At step 302, a medical image of an aorta of a patient is received. Theaorta in the medical image may be an entire aorta of the patient or aportion of the aorta of the patient. The medical image may be directlyreceived from a medical imaging system, such as, e.g., medical imagingsystem 104 of FIG. 1. Alternatively, the medical image may be receivedby loading a previously acquired medical image from storage or memory ofa computer system or receiving a medical image that has been transmittedfrom a remote computer system. In one embodiment, the medical image is aCT medical image, however it should be understood that the medical imagemay be of any suitable modality.

At step 304, the aorta is segmented from the medical image. The aortamay be segmented from the medical image using any suitable approach. Inone embodiment, the aorta is segmented from the medical image byapplying a deep learning model. The deep learning model may be, forexample, a deep image-to-image network. In one embodiment, the deeplearning model is trained and applied as described in further detailbelow with respect to FIG. 4. In one embodiment, the aorta is segmentedfrom the medical image using the method described in U.S. Pat. No.9,715,637, the disclosure of which is incorporated by reference hereinin its entirety. The segmented aorta may be represented, e.g., as a meshof the aorta in the medical image, as a mask assigning labels to eachvoxel of the medical image, as a point cloud of points belonging to theaorta in the medical image, or in any other suitable format.

At step 306, one or more measurement planes are identified on thesegmented aorta. The measurement planes are cross-sectional planes onthe aorta at locations of interest where measurements are to beobtained. In one embodiment, the locations of interest of themeasurement planes are predefined landmarks on the aorta. For example,the locations of interest of the measurement planes may be clinicallysignificant locations on the aorta according to clinically acceptedguidelines. Examples of such clinically significant locations ofinterest of the measurement planes include the aortic annulus, aorticroot, sinotubular junction, tube, aortic arch, descending aorta, andabdominal aorta.

The one or more measurement planes may be identified based on themedical image, the segmented aorta, an aortic centerline of the aorta,and/or other anatomical information of the patient using any suitableapproach. For example, the one or more measurement planes may beidentified using trained deep learning models or other simplermodel-based techniques. The one or more measurement planes may beprovided as a location on the aortic centerline (e.g., a point on theaortic centerline) with a plane normal vector.

In some embodiments, the models may be unable to identify the locationsof interest for the one or more measurement planes due to, e.g., noiseor other imaging artifacts. In such cases, a separate deep learningmodel may be used to predict the locations of interest using otheravailable information.

The aortic centerline of the aorta may be computed by applying a traineddeep learning model, however any suitable approach may be employed. Inone embodiment, the aorta is segmented from the medical image and theaortic centerline is computed for the aorta in a joint and iterativeprocess to increase the accuracy of both, as described below in furtherdetail with respect to FIG. 5.

The anatomical information of the patient used to identify the one ormore measurement planes may include features of the patient such as,e.g., location and size of the heart, location of nearby anatomicallandmarks (e.g., lungs, spine, aortic bifurcation, and subclavianarteries), etc. The anatomical information of the patient may beextracted from the medical image, e.g., by applying separate deeplearning networks trained to identify such anatomical information. Theanatomical information of the patient may be in any suitable format. Forexample, the anatomical information of the patient may be represented asdenoting the location of different landmarks, meshes or masks denotingthe boundary of specific organs, feature vectors embedding informationabout the medical image, etc.

In one embodiment, instead of identifying the one or more measurementplanes from the segmented aorta (segmented at step 304), the one or moremeasurement planes are directly identified from the medical imagereceived at step 302 and step 304 is skipped.

At step 308, at least one measurement is calculated at each of the oneor more measurement planes to evaluate the aorta. The at least onemeasurement may include a diameter of the segmented aorta, however anyother suitable measurement may be calculated. In one embodiment, aplurality of diameters is calculated at different directions for eachmeasurement plane. For example, the at least one measurement may includea maximum, a minimum, and/or an average diameter for each measurementplane. In one embodiment, the at least one measurement includes themaximum diameter and the diameter along a direction orthogonal to themaximum diameter for each measurement plane. FIG. 6 shows an example ofmeasurements taken at different directions on a measurement plane, asdiscussed in more detail below.

The at least one measurement may be calculated using any suitableapproach. In one embodiment, a deep learning model is trained to predictthe at least one measurement. In one embodiment, along with predictingthe at least one measurement, the machine learning model may alsopredict a degree of uncertainty corresponding to the predicted at leastone measurement. The at least one measurement may be compared withmeasurements from a training dataset and, where the at least onemeasurement deviates (e.g., by a threshold amount) from the measurementsfrom the training dataset, user confirmation or intervention isrequested. Actions taken by the user can be saved and applied astraining data for improving the model for future datasets.

At step 310, the aorta of the patient is evaluated based on the at leastone measurement calculated at each of the one or more measurementplanes. In one embodiment, the at least one measurement calculated ateach of the one or more measurement planes is compared with a respectiverange such that measurements outside the range may indicate abnormalityof the aorta. For example, diameters of the aorta of the patient for ameasurement plane may be compared with a patient-specific normal rangeof aortic diameters for that measurement plane. The patient-specificnormal range of aortic diameters may be determined based on the patient,such as, e.g., the age of the patient, the gender of the patient, theethnicity of the patient, and other demographic factors of the patient.The patient-specific normal range of aortic diameters may be aclinically accepted range of aortic diameters, e.g., based on AmericanHeart Association or European Society of Cardiology guidelines.Accordingly, a patient-specific normal range of aortic diameters may bedefined for each of the one or more measurement planes to detectlocations of interest where the aortic diameter of the patient isoutside of the patient-specific normal range.

At step 312, an output is generated based on results of the at least onemeasurement (calculated at step 308) and/or the evaluation of the aorta(performed at step 310). In one embodiment, the output is generated bycausing the results of the at least one measurement and/or theevaluation of the aorta to be displayed on a display device of acomputer system, storing the results of the at least one measurementand/or the evaluation of the aorta on a memory or storage of a computersystem, or by transmitting the results of the at least one measurementand/or the evaluation of the aorta to a remote computer system. FIG. 6shows an exemplary output comprising a display of a cross-sectional viewof an aorta at a measurement plane and two orthogonal diameters of theaorta, as discussed in further detail below.

The calculated measurements may be displayed to a user on a displaydevice in any suitable format. In one example, photorealistic renderingtechniques can be used to generate a high-resolution image of the aorta,while also depicting additional information, such as, e.g., the one ormore measurement planes, the at least one measurement (e.g., averagediameter), and results of the evaluation. In another example, thesegmented aorta, aortic centerline, and locations of the one or moremeasurement planes may be shown overlaid on top of the originallyreceived medical image in a curved multiplanar reconstruction (MPR)view. The MPR view has the advantage of showing the segmented aorta andthe one or more measurement planes in relation to other anatomicalstructures of importance, such as, e.g., the brachiocephalic artery, theleft carotid and subclavian arteries, etc. In one embodiment, the one ormore measurement planes are only on the aortic trunk while a largersubset of the aorta is displayed.

In one embodiment, the output comprises an audio and/or visual alertgenerated in response to results of the evaluation of the aorta to alerta user of the results. For example, the alert may alert the user thatthe at least one measurement is outside the patient-specific normalrange for the patient. In one example, the alert may include a popupwindow notifying the user of the results of the evaluation and promptingthe user to confirm the results of the evaluation. In response, the usermay correct, for example, the segmentation of the aorta, the aorticcenterline, the location and/or orientation of the one or moremeasurement planes, and/or the at least one measurement. The useractions may be saved and used as training data to improve the model forfuture predictions.

In one embodiment, a risk score is calculated indicating a likelihood ofadverse outcomes. The risk score may be calculated, e.g., in response toresults of the evaluation or in response to the user confirming thealert. Based on the calculated risk score and clinical guidelines, aclinical treatment plan for optimal patient outcome may be suggested.For instance, the clinical treatment plan may include a follow-upevaluation with the duration based on the degree of severity indicatedby the risk score, potential additional testing (e.g., additionalimaging or laboratory tests) to confirm the results, or immediateintervention where the risk score indicates high risk. The clinicaltreatment plan may be based on established clinical guidelines, andpresented to the user with a link to published clinical decision makingpathways supporting the plan along with known statistics of thedifferent risks and outcomes associated with each item in the plan. Inone embodiment, the clinical treatment plan may be sent to a patientmanagement system to automatically trigger communication with thepatient to facilitate execution of the clinical treatment plan (e.g., tofacilitate setting up an appointment).

The steps of method 300 can be performed fully automatic and/orsemi-automatic. In the fully automatic case, the models automaticallyperform all steps and generate results without additional user input.The results can be in any suitable format. In one embodiment, theresults are in a format that can be directly entered into a hospitaldata management system. In the semi-automatic case, the outputs of themodels (e.g., the segmentation of the aorta, the identification of thelocation/orientation of the one or more measurement planes, and/or thecalculation of the at least one measurement, etc.) can be edited by theuser. For example, over-segmentation of the aorta at step 304 can becorrected by the user. The corrected results may be entered into thehospital management system. The user input may be saved and applied astraining data to improve future predictions.

In some embodiments, the machine learning model trained to predict acertain result, such as, e.g., segmentation of the aorta or calculationof the at least one measurement, may provide inaccurate results due tonoise or artifacts in portions of the medical image. Instead ofproviding inaccurate results, a user may be prompted to manually enteror correct the results.

FIG. 4 shows a workflow 400 for training and applying a machine learningmodel for segmenting an aorta from a medical image, in accordance withone or more embodiments. Steps 402-404 show an offline or training stagefor training a machine learning model. Steps 406-410 show an online orinference stage for applying the trained machine learning model on newlyreceived input medical images. In one embodiment, step 304 of FIG. 3 isperformed by performing the steps of the inference state (steps406-410). The inference stage (steps 406-410) can be repeated for eachnewly received input medical image(s).

At step 402, during a training stage, training images including an aortaare received. The training images are medical images in a modalitycorresponding to the modality of the input medical image to be analyzedduring the inference stage (at step 406). For example, the modality maybe computed tomography (CT), magnetic resonance (MR), DynaCT,ultrasound, x-ray, positron emission tomography (PET), etc. In oneembodiment, the training images can be received by loading a number ofpreviously stored medical training images from a database of medicalimages.

The training images may comprise real training images annotated toindicate the location of the aorta. The annotations may be in anysuitable form. In one embodiment, the annotations comprise, e.g., amesh, a mask assigning a label to each voxel in the training images, ora point cloud of points belonging to the aorta in the training images.

The training images may also comprise synthetic training images. Thesynthetic images may be generated using, e.g., imaging of phantomdatasets or imaging simulators, which generate synthetic images based onthe size and location of different organs. In some embodiments, thesynthetic images are generated using deep learning models, such as,e.g., a generative adversarial network (GAN) trained to generaterealistic looking images using known images from other imagingmodalities. For example, a GAN may be trained to generate CT trainingimages of the aorta from MRI images. This model may be used to produce alarge set of training images.

The training images may further comprise augmented training imagesgenerated using data augmentation techniques. In one embodiment, theaugmented training images may be generated by applying transformationsto real training images and their annotations. Any suitabletransformation may be applied, such as, e.g., adding a degree of noiseto the real training images, scaling the real training image, rotatingthe real training image, etc. In one embodiment, the transformations arelocally varied to simulate pathologies or conditions. For example, thereal training images and their annotations may be locally dilated tosimulate the effect of an abdominal aneurysm. Other transformations maybe applied to simulate, e.g., an aortic stenosis.

In one embodiment, the training images may be associated with additionaldata for training the machine learning model. For example, theadditional data associated with the training images may include theadditional anatomical information, as described above with respect tostep 306 of FIG. 3. In another example, the additional data associatedwith the training images may also include a genetic profile of a patientassociated with the training image.

In one embodiment, the training images include an input image previouslyreceived during the inference stage where the user have provided input(e.g., to define the segmentation). Accordingly, the user input isprovided as the ground truth for the input image. The input image may befrom a local population of a healthcare center so that the model learnsto adapt better to the population of the healthcare center, or from aglobal population of all (or a plurality of) healthcare centers receivedfrom a remote location to improve the future performance of thosehealthcare centers.

At step 404, a machine learning model is trained to segment an aortafrom a medical image based on the training images (and additional data).In one embodiment, the machine learning model is a deep image-to-imagenetwork, which is trained to learn the correspondence between thetraining images and their ground truths. The deep image-to-image networklearns to minimize the difference between the predicted segmentation(e.g., the segmentation mask) and the ground truth (e.g., the groundtruth mask), which can be accomplished using different mathematical lossfunctions (e.g., known loss functions).

At step 406, during an inference stage, an input medical image of apatient is received. The input medical image comprises an aorta.Additional data may also be received, such as, e.g., additionalanatomical information of the patient, genetic profile information ofthe patient, etc. In one embodiment, the input medical image is themedical image received at step 302 of FIG. 3.

At step 408, a segmentation of the aorta from the input medical image ispredicted using the trained machine learning model. The input medicalimage is input to the trained machine learning model, and the trainedmachine learning model generates the segmentation of the aorta based onthe input medical image. In some embodiments, the additional patientdata, such as additional anatomical information of the patient, geneticprofile information of the patient, etc., may also be input to thetrained machine learning model and used to generate the segmentation ofthe aorta. The segmentation may be in any suitable form, such as, e.g.,a mesh of the aorta in the input medical image, a mask assigning labelsto each voxel of the input medical image, or a point cloud of pointsbelonging to the aorta in the input medical image.

At step 410, the segmentation of the aorta is output. In one embodiment,the segmentation of the aorta is output by returning the segmentation tostep 304 of FIG. 3. In some embodiments, the segmentation of the aortacan be output by displaying the segmentation on a display device of acomputer system, storing the segmentation on a memory or storage of acomputer system, or by transmitting the segmentation to a remotecomputer system.

It should be understood that once the machine learning model is trainedduring the training stage, the steps 406-410 of the inference stage canbe repeated for each newly received input medical image(s).

It should further be understood that workflows similar to workflow 400can be performed to train and apply a machine learning model to performtasks described herein using suitable training data. For example,workflows similar to workflow 400 can be performed to train and apply amachine learning model to compute an aortic centerline, to identify theadditional anatomical information, and to identify the one or moremeasurement planes at step 306 of FIG. 3, to calculate the at least onemeasurement at step 308 of FIG. 3, or to generate synthetic images instep 402 of FIG. 4 using suitable training data.

FIG. 5 shows a workflow 500 of a joint and iterative process forsegmenting an aorta from a medical image and computing an aorticcenterline of the aorta, in accordance with one or more embodiments. Inone embodiment, workflow 500 may be performed to segment the aorta fromthe medical image at step 304 of FIG. 3 and to compute the aorticcenterline of the aorta at step 306 of FIG. 3. Advantageously, the jointand iterative process of workflow 500 provides a more accurate segmentedaorta and aortic centerline.

At step 502, a medical image including an aorta is received.

At step 504, the aorta is segmented from the medical image to provide aninitial aortic mask. For example, the aorta may be segmented from themedical image using a trained machine learning model, as trained andapplied according to FIG. 4. While the segmented aorta is represented asan aortic mask at step 504, it should be understood that the segmentedaorta may be represented in any suitable format (e.g., a mesh or a pointcloud).

At step 506, an initial aortic centerline of the aorta is computed. Theaortic centerline may be computed using any suitable approach, such as atrained deep learning model.

At step 508, a refined aortic mask and a refined aortic centerline aredetermined. In one embodiment, the refined aortic mask may first bedetermined by refining the initial aortic mask using a region-wise modelapplied in the neighborhood of the initial aortic centerline. Therefined aortic centerline may then be determined using, e.g., a traineddeep learning model based on the refined aortic mask. Step 508 may beiteratively repeated any number of times (e.g., a predetermined numberof times) until a suitable segmentation of the aorta and aorticcenterline are determined.

It should be understood that steps 504 and 506 may be performed in anyorder. For example, in one embodiment, an initial aortic centerline isfirst computed directly from the medical image at step 506 and then aninitial aortic mask is determined from the medical image based on theinitial aortic centerline at step 504. Accordingly, in this embodiment,at step 508, a refined aortic centerline is first determined using atrained deep learning model based on the initial aortic mask and arefined aortic mask is then determined using the region-wise model basedon the refined aortic centerline.

FIG. 6 shows a user interface 600 depicting measurements and results ofan evaluation of an aorta, in accordance with one or more embodiments.In one embodiment, user interface 600 may be an output at step 312 ofFIG. 3. User interface 600 shows a CT medical image 602 depicting across-sectional view of an aorta at a measurement plane. A segmentedaortic mask 602 is overlaid thereon with measurements 606 and 608representing orthogonal diameters of segmented aortic mask 602 that werecalculated. Measurement result 610 is shown indicating a diameter of37.7 millimeters for measurement 608.

Systems, apparatuses, and methods described herein may be implementedusing digital circuitry, or using one or more computers using well-knowncomputer processors, memory units, storage devices, computer software,and other components. Typically, a computer includes a processor forexecuting instructions and one or more memories for storing instructionsand data. A computer may also include, or be coupled to, one or moremass storage devices, such as one or more magnetic disks, internal harddisks and removable disks, magneto-optical disks, optical disks, etc.

Systems, apparatus, and methods described herein may be implementedusing computers operating in a client-server relationship. Typically, insuch a system, the client computers are located remotely from the servercomputer and interact via a network. The client-server relationship maybe defined and controlled by computer programs running on the respectiveclient and server computers.

Systems, apparatus, and methods described herein may be implementedwithin a network-based cloud computing system. In such a network-basedcloud computing system, a server or another processor that is connectedto a network communicates with one or more client computers via anetwork. A client computer may communicate with the server via a networkbrowser application residing and operating on the client computer, forexample. A client computer may store data on the server and access thedata via the network. A client computer may transmit requests for data,or requests for online services, to the server via the network. Theserver may perform requested services and provide data to the clientcomputer(s). The server may also transmit data adapted to cause a clientcomputer to perform a specified function, e.g., to perform acalculation, to display specified data on a screen, etc. For example,the server may transmit a request adapted to cause a client computer toperform one or more of the steps or functions of the methods andworkflows described herein, including one or more of the steps orfunctions of FIGS. 2-5. Certain steps or functions of the methods andworkflows described herein, including one or more of the steps orfunctions of FIGS. 2-5, may be performed by a server or by anotherprocessor in a network-based cloud-computing system. Certain steps orfunctions of the methods and workflows described herein, including oneor more of the steps of FIGS. 2-5, may be performed by a client computerin a network-based cloud computing system. The steps or functions of themethods and workflows described herein, including one or more of thesteps of FIGS. 2-5, may be performed by a server and/or by a clientcomputer in a network-based cloud computing system, in any combination.

Systems, apparatus, and methods described herein may be implementedusing a computer program product tangibly embodied in an informationcarrier, e.g., in a non-transitory machine-readable storage device, forexecution by a programmable processor; and the method and workflow stepsdescribed herein, including one or more of the steps or functions ofFIGS. 2-5, may be implemented using one or more computer programs thatare executable by such a processor. A computer program is a set ofcomputer program instructions that can be used, directly or indirectly,in a computer to perform a certain activity or bring about a certainresult. A computer program can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment.

A high-level block diagram of an example computer 702 that may be usedto implement systems, apparatus, and methods described herein isdepicted in FIG. 7. Computer 702 includes a processor 704 operativelycoupled to a data storage device 712 and a memory 710. Processor 704controls the overall operation of computer 702 by executing computerprogram instructions that define such operations. The computer programinstructions may be stored in data storage device 712, or other computerreadable medium, and loaded into memory 710 when execution of thecomputer program instructions is desired. Thus, the method and workflowsteps or functions of FIGS. 2-5 can be defined by the computer programinstructions stored in memory 710 and/or data storage device 712 andcontrolled by processor 704 executing the computer program instructions.For example, the computer program instructions can be implemented ascomputer executable code programmed by one skilled in the art to performthe method and workflow steps or functions of FIGS. 2-5. Accordingly, byexecuting the computer program instructions, the processor 704 executesthe method and workflow steps or functions of FIGS. 2-5. Computer 704may also include one or more network interfaces 706 for communicatingwith other devices via a network. Computer 702 may also include one ormore input/output devices 708 that enable user interaction with computer702 (e.g., display, keyboard, mouse, speakers, buttons, etc.).

Processor 704 may include both general and special purposemicroprocessors, and may be the sole processor or one of multipleprocessors of computer 702. Processor 704 may include one or morecentral processing units (CPUs), for example. Processor 704, datastorage device 712, and/or memory 710 may include, be supplemented by,or incorporated in, one or more application-specific integrated circuits(ASICs) and/or one or more field programmable gate arrays (FPGAs).

Data storage device 712 and memory 710 each include a tangiblenon-transitory computer readable storage medium. Data storage device712, and memory 710, may each include high-speed random access memory,such as dynamic random access memory (DRAM), static random access memory(SRAM), double data rate synchronous dynamic random access memory (DDRRAM), or other random access solid state memory devices, and may includenon-volatile memory, such as one or more magnetic disk storage devicessuch as internal hard disks and removable disks, magneto-optical diskstorage devices, optical disk storage devices, flash memory devices,semiconductor memory devices, such as erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), compact disc read-only memory (CD-ROM), digital versatile discread-only memory (DVD-ROM) disks, or other non-volatile solid statestorage devices.

Input/output devices 708 may include peripherals, such as a printer,scanner, display screen, etc. For example, input/output devices 708 mayinclude a display device such as a cathode ray tube (CRT) or liquidcrystal display (LCD) monitor for displaying information to the user, akeyboard, and a pointing device such as a mouse or a trackball by whichthe user can provide input to computer 702.

Any or all of the systems and apparatus discussed herein, includingelements of workstation 102 of FIG. 1, may be implemented using one ormore computers such as computer 702.

One skilled in the art will recognize that an implementation of anactual computer or computer system may have other structures and maycontain other components as well, and that FIG. 7 is a high levelrepresentation of some of the components of such a computer forillustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method for evaluating an aorta of a patient, comprising: receivinga medical image of an aorta of a patient; segmenting the aorta from themedical image; identifying one or more measurement planes on thesegmented aorta; calculating at least one measurement at each of the oneor more measurement planes; and evaluating the aorta of the patientbased on the at least one measurement calculated at each of the one ormore measurement planes.
 2. The method of claim 1, wherein identifyingone or more measurement planes on the segmented aorta comprises:identifying an aortic centerline of the aorta; and identifying one ormore locations on the aortic centerline and corresponding plane normalvectors as the one or more measurement planes.
 3. The method of claim 2,further comprising: refining the segmented aorta based on the aorticcenterline; and refining the aortic centerline based on the refinedsegmented aorta.
 4. The method of claim 1, wherein calculating at leastone measurement at each of the one or more measurement planes comprises:calculating at least one diameter of the segmented aorta at each of theone or more measurement planes.
 5. The method of claim 4, whereincalculating at least one diameter of the segmented aorta at each of theone or more measurement planes comprises: calculating at least one of aminimum diameter, a maximum diameter, and an average diameter of thesegmented aorta at each of the one or more measurement planes.
 6. Themethod of claim 1, wherein evaluating the aorta of the patient based onthe at least one measurement calculated at each of the one or moremeasurement planes comprises: comparing the at least one measurementcalculated at each respective measurement plane of the one or moremeasurement planes with a patient-specific range.
 7. The method of claim6, further comprising: generating an alert indicating results of theevaluating in response to the comparing.
 8. The method of claim 6,further comprising: calculating a risk score in response to thecomparing; and causing a clinical treatment plan to be presented to auser based on the risk score.
 9. The method of claim 1, furthercomprising: causing results of the calculating and/or the evaluating tobe displayed on a display device.
 10. An apparatus for evaluating anaorta of a patient, comprising: means for receiving a medical image ofan aorta of a patient; means for segmenting the aorta from the medicalimage; means for identifying one or more measurement planes on thesegmented aorta; means for calculating at least one measurement at eachof the one or more measurement planes; and means for evaluating theaorta of the patient based on the at least one measurement calculated ateach of the one or more measurement planes.
 11. The apparatus of claim10, wherein the means for identifying one or more measurement planes onthe segmented aorta comprises: means for identifying an aorticcenterline of the aorta; and means for identifying one or more locationson the aortic centerline and corresponding plane normal vectors as theone or more measurement planes.
 12. The apparatus of claim 11, furthercomprising: means for refining the segmented aorta based on the aorticcenterline; and means for refining the aortic centerline based on therefined segmented aorta.
 13. The apparatus of claim 10, wherein themeans for calculating at least one measurement at each of the one ormore measurement planes comprises: means for calculating at least onediameter of the segmented aorta at each of the one or more measurementplanes.
 14. The apparatus of claim 13, wherein the means for calculatingat least one diameter of the segmented aorta at each of the one or moremeasurement planes comprises: means for calculating at least one of aminimum diameter, a maximum diameter, and an average diameter of thesegmented aorta at each of the one or more measurement planes.
 15. Anon-transitory computer readable medium storing computer programinstructions for evaluating an aorta of a patient, the computer programinstructions when executed by a processor cause the processor to performoperations comprising: receiving a medical image of an aorta of apatient; segmenting the aorta from the medical image; identifying one ormore measurement planes on the segmented aorta; calculating at least onemeasurement at each of the one or more measurement planes; andevaluating the aorta of the patient based on the at least onemeasurement calculated at each of the one or more measurement planes.16. The non-transitory computer readable medium of claim 15, whereinidentifying one or more measurement planes on the segmented aortacomprises: identifying an aortic centerline of the aorta; andidentifying one or more locations on the aortic centerline andcorresponding plane normal vectors as the one or more measurementplanes.
 17. The non-transitory computer readable medium of claim 15,wherein evaluating the aorta of the patient based on the at least onemeasurement calculated at each of the one or more measurement planescomprises: comparing the at least one measurement calculated at eachrespective measurement plane of the one or more measurement planes witha patient-specific range.
 18. The non-transitory computer readablemedium of claim 17, the operations further comprising: generating analert indicating results of the evaluating in response to the comparing.19. The non-transitory computer readable medium of claim 17, theoperations further comprising: calculating a risk score in response tothe comparing; and causing a clinical treatment plan to be presented toa user based on the risk score.
 20. The non-transitory computer readablemedium of claim 15, the operations further comprising: causing resultsof the calculating and/or the evaluating to be displayed on a displaydevice.